Mobile and wearable devices such as smartphones, tablets, smart watches, and activity trackers increasingly carry one or more sensors such as accelerometers, gyroscopes, magnetometers, barometers, microphones, and GPS receivers that can be used either singly or jointly to detect a user's context such as motion activities of the user, voice activities of or about the user, and a spatial environment of the user. Previous research work on motion activities has considered the classification of basic locomotion activities of a user such as walking, jogging, and cycling. Voice detection uses microphone recordings to detect human speech from silence in the presence of background noise and is used in applications such as audio conferencing, variable rate speech codecs, speech recognition, and echo cancellation. The detection of a mobile device user's spatial environment from audio recordings has been investigated for determining environment classifications of the user such as in the office, on the street, at a stadium, at the beach etc.
In most context detection tasks, data from one sensor is used. The accelerometer is typically used for motion activity detection while the microphone is used for voice activity detection and spatial environment detection.
These prior art detection methods provide for a deterministic output in the form of a detected class from a set of specific classes for motion activities or acoustic environments, as described above. However, the determination of the user's context using such prior art techniques may not be as accurate as would be ideally desirable, and moreover, does not allow for more complex determinations about the user's context. Consequently, further development in this area is needed.